The internet of Things (IoT) is a concept of connecting things to the Internet via various identifying and sensing devices so that remote control or direct object-to-object communication can be achieved for the purpose of intelligent identification management. Therefore, the sensing devices play a very important role in the IoT. As an example, sensing devices such as radio frequency identification (RFID) tags, infrared sensors, global positioning system (GPS) devices can be disposed on various objects (e.g., electric power grids, roads, buildings, household electric appliances, or the like) and connected with the Internet so that automatic identification and transparent management of the objects as well as information sharing can be achieved during the processes of production, distribution, and consumption of the objects. Hence, the IoT can be applied to various kinds of applications, including applications in transportation, logistics, intelligent environment management, health care, personal affairs, and so on.
What accompanied with the rapid development of the IoT is the generation of massive sensing data. The amount of the sensing data is still increasing with the time elapses. The massive sensing data occupy large memory spaces of hard disks and make the backup management difficult, so the data stored must be compressed at regularly to save the memory spaces of the hard disks.
The compression technology built in common databases (e.g., Oracle) may be used to compress the sensing data. Although this can reduce the memory spaces occupied in the hard disks, the compression technology built in the databases is not specifically used for compression of the sensing data. Consequently, the memory spaces that can be saved are limited. Furthermore, if a user wants to query only a part of the sensing data compressed by this kind of compression technology, he or she must decompress all the compressed sensing data, which makes the query time too long for the user.
Accordingly, an urgent need exists in the art to provide a technology that can solve the problems of a low data compression rate and a too long query time.